class_docstr: An implementation of `"HOPE" <https://www.kdd.org/kdd2016/papers/files/rfp0184-ouA.pdf>`_
  from the KDD '16 paper "Asymmetric Transitivity Preserving Graph Embedding". The procedure uses
  sparse SVD on the neighbourhood overlap matrix. The singular value rescaled left and right
  singular vectors are used as the node embeddings after concatenation.
  
  Args:
      dimensions (int): Dimensionality of embedding. Default is 128.
      seed (int): Random seed value. Default is 42.
docstr: Creating a target similarity matrix.
docstr_tok: ["Creating","a","target","similarity","matrix","."]
code: def _create_target(self, graph):
      """
      Creating a target similarity matrix.
      """
      number_of_nodes = graph.number_of_nodes()
      A = nx.adjacency_matrix(graph, nodelist=range(number_of_nodes))
      S = sps.coo_matrix(A.dot(A), dtype=np.float32)
      return S
  
file_path: karateclub/karateclub/node_embedding/neighbourhood/hope.py
imports: import numpy
  import networkx
  import scipy.sparse
  from karateclub.estimator import Estimator
label_desc: Use image node_id 2 for calling the HOPE obj's underlying member method code with example usage: obj._create_target(graph) and returns: S